A DNN-Based Cross-Domain Recommender System for Alleviating Cold-Start Problem in E-Commerce

Abstract

Many applications use recommender systems to predict user preferences, improve user experience, and increase the amount of sales. However, because of the cold-start problem, it is not easy to recommend items to new users accurately. Recommendation performance degrades in the case of users with little interaction, in particular latent users who have never used the service. To alleviate the cold-start problem, we develop a framework that combines an online shopping domain with information from an Ads platform. Our framework employs deep learning to build a cross-domain recommender system based on shared users in these two domains. This is the first attempt that models users based on shared users in online shopping and Ads domains for solving the user-cold start problem. We apply Word2Vec to turn textual information on users and items into latent vectors as their representations. The experimental results show the effectiveness of deep neural approaches with knowledge transferred from another domain for the cold-start problem. Textual information may contain useless information, and Word2Vec cannot capture some structural and semantic correlations between different users. Therefore, we propose R-metapath2Vec to enhance user modeling and use the Stacking model to integrate these two kinds of user representations. The experimental results demonstrate the effectiveness of our integration model: our framework can recommend products to users of another domain through Ads distribution in a more accurate level.